An Improved Model for Node Discovery using Election Algorithm in Wireless Sensor Network

Ifedotun Roseline Idowu *

Department of Computer Science, Federal College of Animal Health and Production Technology, Moor Plantation, Ibadan, Nigeria.

Kayode Okewale

Department of Computer and Information Sciences, Northumbria University, Newcastle, England.

Rodrigue Ngomsi

Department of Computer and Information Sciences, Northumbria University, Newcastle, England.

*Author to whom correspondence should be addressed.


Abstract

Mobile wireless devices are constrained based on resources. Computation offloading provides a unique method for taking advantage of multiple mobile wireless devices to save energy and increase performance of these devices. In order to build a robust serious   solution for a mobile wireless sensor network. There must be a scheme to ensure that the resources of the mobile wireless sensor network is managed adequately. However, computational offloading scheme was proposed by researchers. But this solution was dependent on a super node which manages the offloading process and is required to be online on the network at all time which consume time. The objectives of this study is to create a “broadcast and receive” table that can access a given node at a particular time interval without the need of having a super node online all the time. This method ensures that as nodes enter the network, they announce their resources which is then saved in the table. This information is updated at a given time interval by a monitoring service. When a node is to be removed from the network, the details of the resources of the node is removed from the table. The focus of the study is to evaluate the resource aware of node discovery in wireless sensor network.

Keywords: WSN, MCC, Computational offloading, node discovery, device profiler, “broadcast and receive”


How to Cite

Idowu, Ifedotun Roseline, Kayode Okewale, and Rodrigue Ngomsi. 2022. “An Improved Model for Node Discovery Using Election Algorithm in Wireless Sensor Network”. Asian Journal of Research in Computer Science 14 (3):25-38. https://doi.org/10.9734/ajrcos/2022/v14i330341.

Downloads

Download data is not yet available.